



'''simple docstring'''






import numpy

# List of input, output pairs
lowerCAmelCase:   List[Any]					=		(
    ((5, 2, 3), 1_5),
    ((6, 5, 9), 2_5),
    ((1_1, 1_2, 1_3), 4_1),
    ((1, 1, 1), 8),
    ((1_1, 1_2, 1_3), 4_1),
)
lowerCAmelCase:   Optional[Any]					=		(((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
lowerCAmelCase:   Union[str, Any]					=		[2, 4, 1, 5]
lowerCAmelCase:   int					=		len(train_data)
lowerCAmelCase:   Optional[int]					=		0.009
def  lowerCamelCase__			(		_A					,     _A="train"							):
  return calculate_hypothesis_value(_A					,     _A							) - output(
      _A					,     _A							)
def  lowerCamelCase__			(		_A							):
  a		:   List[str]	  =	0
  for i in range(len(_A							) - 1							):
    hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
  hyp_val += parameter_vector[0]
  return hyp_val
def  lowerCamelCase__			(		_A					,     _A							):
  if data_set == "train":
    return train_data[example_no][1]
  elif data_set == "test":
    return test_data[example_no][1]
  return None
def  lowerCamelCase__			(		_A					,     _A							):
  if data_set == "train":
    return _hypothesis_value(train_data[example_no][0]							)
  elif data_set == "test":
    return _hypothesis_value(test_data[example_no][0]							)
  return None
def  lowerCamelCase__			(		_A					,     _A=m							):
  a		:   List[str]	  =	0
  for i in range(_A							):
    if index == -1:
      summation_value += _error(_A							)
    else:
      summation_value += _error(_A							) * train_data[i][0][index]
  return summation_value
def  lowerCamelCase__			(		_A							):
  a		:   List[Any]	  =	summation_of_cost_derivative(_A					,     _A							) / m
  return cost_derivative_value
def  lowerCamelCase__			(		):
  global parameter_vector
  # Tune these values to set a tolerance value for predicted output
  a		:   Optional[int]	  =	0.000002
  a		:   List[Any]	  =	0
  a		:   int	  =	0
  while True:
    j += 1
    a		:   str	  =	[0, 0, 0, 0]
    for i in range(0					,     len(_A							)							):
      a		:   Dict	  =	get_cost_derivative(i - 1							)
      a		:   Optional[Any]	  =	(
          parameter_vector[i] - LEARNING_RATE * cost_derivative
      )
    if numpy.allclose(
        _A					,     _A					,     atol=_A					,     rtol=_A					,     ):
      break
    a		:   Dict	  =	temp_parameter_vector
  print(('Number of iterations:', j)							)
def  lowerCamelCase__			(		):
  for i in range(len(_A							)							):
    print(('Actual output value:', output(_A					,     'test'							))							)
    print(('Hypothesis output:', calculate_hypothesis_value(_A					,     'test'							))							)


if __name__ == "__main__":
     run_gradient_descent()
     print('\nTesting gradient descent for a linear hypothesis function.\n')
     test_gradient_descent()




'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )